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A Machine Learning-Based Method for Identifying Critical Distance Relays for Transient Stability Studies

Author

Listed:
  • Ramin Vakili

    (The School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ 85287-5706, USA)

  • Mojdeh Khorsand

    (The School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ 85287-5706, USA)

Abstract

Protective relays play a crucial role in defining the dynamic responses of power systems during and after faults. Therefore, modeling protective relays in stability studies is crucial for enhancing the accuracy of these studies. Modeling all the relays in a bulk power system is a challenging task due to the limitations of stability software and the difficulties of keeping track of the changes in the setting information of these relays. Distance relays are one of the most important protective relays that are not properly modeled in current practices of stability studies. Hence, using the Random Forest algorithm, a fast machine learning-based method is developed in this paper that identifies the distance relays required to be modeled in stability studies of a contingency, referred to as critical distance relays (CDRs). GE positive sequence load flow analysis (PSLF) software is used to perform stability studies. The method is tested using 2018 summer peak load data of Western Electricity Coordinating Council (WECC) for various system conditions. The results illustrate the great performance of the method in identifying the CDRs. They also show that to conduct accurate stability studies, only modeling the CDRs suffices, and there is no need for modeling all the distance relays.

Suggested Citation

  • Ramin Vakili & Mojdeh Khorsand, 2022. "A Machine Learning-Based Method for Identifying Critical Distance Relays for Transient Stability Studies," Energies, MDPI, vol. 15(23), pages 1-28, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:8841-:d:981683
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    References listed on IDEAS

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    1. David L. Olson & Dursun Delen, 2008. "Advanced Data Mining Techniques," Springer Books, Springer, number 978-3-540-76917-0, February.
    2. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    3. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
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    Cited by:

    1. Aleksandr Kulikov & Anton Loskutov & Dmitriy Bezdushniy & Ilya Petrov, 2023. "Decision Tree Models and Machine Learning Algorithms in the Fault Recognition on Power Lines with Branches," Energies, MDPI, vol. 16(14), pages 1-19, July.

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